Deep learning-based calibration of resistance factors for pile groups with load tests Article Swipe
Yuting Zhang
,
Jinsong Huang
,
Jiawei Xie
,
Shui‐Hua Jiang
,
Cheng Zeng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11440-025-02634-7
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11440-025-02634-7
Resistance factors for pile groups are typically derived using empirical methods that do not directly account for system redundancy and overlook the correlation between individual piles, which are inherently influenced by the spatial variability of soils. While rigorous three-dimensional (3D) random finite difference (RFD) or random finite element (RFE) analyses could potentially address these issues, they are constrained by significant computational demands. Therefore, this paper proposes a deep learning-based approach for calibrating resistance factors for pile groups with individual pile load tests. Specifically, a surrogate model based on a convolutional neural network (CNN) is proposed, which is trained and validated using the database generated by RFD analyses. The trained model is further used to derive pile resistances in spatially variable soils. Finally, the resistance factors are calibrated by counting and conditional probability based on the outcomes of load test results. The proposed approach is demonstrated using a pile group example. Results show that the proposed approach effectively captures the impacts of load test results and their corresponding locations, as well as the spatial variability of soil properties, on resistance factors.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11440-025-02634-7
- https://link.springer.com/content/pdf/10.1007/s11440-025-02634-7.pdf
- OA Status
- hybrid
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410959447
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410959447Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11440-025-02634-7Digital Object Identifier
- Title
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Deep learning-based calibration of resistance factors for pile groups with load testsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-02Full publication date if available
- Authors
-
Yuting Zhang, Jinsong Huang, Jiawei Xie, Shui‐Hua Jiang, Cheng ZengList of authors in order
- Landing page
-
https://doi.org/10.1007/s11440-025-02634-7Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s11440-025-02634-7.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11440-025-02634-7.pdfDirect OA link when available
- Concepts
-
Solid mechanics, Pile, Calibration, Resistance (ecology), Resistance Factors, Geotechnical engineering, Materials science, Structural engineering, Geology, Engineering, Mathematics, Composite material, Statistics, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 39 |
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| abstract_inverted_index.counting | 129 |
| abstract_inverted_index.database | 103 |
| abstract_inverted_index.demands. | 62 |
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| citation_normalized_percentile.is_in_top_10_percent | False |